SIR: Deterministic protein inference from peptides assigned to MS data

Matthiesen, Rune; Amorim, António; Aloria, Kerman; Fullaondo, Asier; Carvalho, Ana Sofía; Arizmendi, Jesús M.

doi:10.1016/j.jprot.2012.05.010

Cited by 27 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Protein inference is often complicated by the fact that many tryptic peptides are shared among proteins. Moreover, mass accuracy used for database-dependent search, the specific sequence database used for the search, the filtering criteria such as minimum number of peptides identified, and False Discovery Rate (FDR) cutoff can also dramatically change the proteins and peptides identified in the final report and can be a reason for lost information and low reproducibility when validation is performed in another laboratory that uses a different data analysis pipeline [43,44]. Besides protein inference, the protein state is another issue in bottom-up proteomics, which often does not provide accurate information on the site location of PTMs, and even if the site location is well determined the quantitative value cannot be directly linked to quantitative values of protein states [45].…”

Section: Proteomics-based Technologiesmentioning

confidence: 99%

Overview of proteomics studies in obstructive sleep apnea

et al. 2015

Self Cite

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) is an underdiagnosed common public health concern causing deleterious effects on metabolic and cardiovascular health. Although much has been learned regarding the pathophysiology and consequences of OSA in the past decades, the molecular mechanisms associated with such processes remain poorly defined. The advanced high-throughput proteomics-based technologies have become a fundamental approach for identifying novel disease mediators as potential diagnostic and therapeutic targets for many diseases, including OSA. Here, we briefly review OSA pathophysiology and the technological advances in proteomics and the first results of its application to address critical issues in the OSA field.

show abstract

Section: Proteomics-based Technologiesmentioning

confidence: 99%

Overview of proteomics studies in obstructive sleep apnea

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…2B). Although others have proposed and implemented more nuanced levels of evidence groups (2,9,26,27), this simple three-tiered approach covers all possibilities and is easily interpretable. IDGroup is a PSM quality rank that combines the spectral match score with PSM q-values as described in gene-centric approach by gpGrouper and protein-centric approach by MaxQuant (without cross-species peptide elimination).…”

Section: Fig 1 Gene-centric Grouping Is a Robust Methods For Inferenmentioning

confidence: 99%

gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data

Saltzman

Leng

Bhatt

et al. 2018

Molecular & Cellular Proteomics

View full text Add to dashboard Cite

In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy compared with conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient-derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.

show abstract

“…The FDR was set to 0.01 for peptide and protein identifications in both MaxQuant and mVEMS. Only proteins in evidence groups one to three, as defined by Matthiesen et al (37), were considered in subsequent quantitative analysis.…”

Section: Methodsmentioning

confidence: 99%

“…The database-dependent search of the MS/MS spectra from the nuclear, mitochondrial, and cytosolic crude fractions resulted in a set of 18,816 proteins using a 1% FDR cutoff. Of these, 17,745 proteins were placed in evidence groups one to three as defined by Matthiesen et al (37). Collapsing the proteins into the corresponding coding genes yielded 5181 proteins, 5065 of which were not common contaminants.…”

Section: Lymphocyte-derived Cell Lines Respond Differently Tomentioning

confidence: 99%

“…2A-2C were filtered to include proteins that are not common contaminants seen by MS and with evidence groups 1 to 3. This means that proteins not required to explain the MS data, that is, evidence groups 4 to 5, were filtered out and only peptide spectra assignments with maximum scores were taken into account for protein inference (37). Furthermore, to provide a more compact view and because we have incomplete knowledge of how specific protein isoforms function, the protein isoforms were collapsed into the corresponding coding genes for the presented heatmaps.…”

Section: Test Followed By Benjamini and Hochberg Correction)mentioning

confidence: 99%

See 1 more Smart Citation

Global Mass Spectrometry and Transcriptomics Array Based Drug Profiling Provides Novel Insight into Glucosamine Induced Endoplasmic Reticulum Stress

Carvalho

Ribeiro

Voabil

et al. 2014

Molecular & Cellular Proteomics

Self Cite

View full text Add to dashboard Cite

We investigated the molecular effects of glucosamine supplements, a popular and safe alternative to nonsteroidal anti-inflammatory drugs, for decreasing pain, inflammation, and maintaining healthy joints. Numerous studies have reported an array of molecular effects after glucosamine treatment. We questioned whether the differences in the effects observed in previous studies were associated with the focus on a specific subproteome or with the use of specific cell lines or tissues. To address this question, global mass spectrometry-and transcription arraybased glucosamine drug profiling was performed on malignant cell lines from different stages of lymphocyte development. We combined global label-free MS-based protein quantitation with an open search for modifications to obtain the best possible proteome coverage. Our data were largely consistent with previous studies in a variety of cellular models. We mainly observed glucosamine induced O-GlcNAcylation/O-GalNAcylation (O-HexNAcylation); however, we also observed global and local changes in acetylation, methylation, and phosphorylation. (4,5). The clinical relevance of GlcN, a dietary supplement used in osteoarthritis patients, is unclear (6). Previous investigations of long-term GlcN administration did not reveal risks or concerns (7), which is also consistent with recent studies (8). However, in recent clinical studies, GlcN oral administration (8) did not demonstrate any benefits, and the concentration used in vitro was not comparable with the levels observed in the plasma in vivo after the oral administration of GlcN, which raised skepticism (8, 9). Therefore,

show abstract

SIR: Deterministic protein inference from peptides assigned to MS data

Cited by 27 publications

References 16 publications

Overview of proteomics studies in obstructive sleep apnea

Overview of proteomics studies in obstructive sleep apnea

gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data

Global Mass Spectrometry and Transcriptomics Array Based Drug Profiling Provides Novel Insight into Glucosamine Induced Endoplasmic Reticulum Stress

Contact Info

Product

Resources

About